import tensorflow as tf
from tensorflow.keras.datasets import mnist
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.keras.layers import LSTM, Dense, Input,Concatenate, Bidirectional, BatchNormalization, Dropout, Attention
from tensorflow.keras.models import Sequential, Model
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_test.shape
(10000, 28, 28)
y_train.shape
(60000,)
x_train = x_train.reshape(-1, 28, 28) / 255.0
x_test = x_test.reshape(-1, 28, 28) / 255.0
num_viz = 10
plt.figure(figsize=(10,10))
for i in range(num_viz):
ax = plt.subplot(1, 10, i+1)
plt.imshow(x_train[i], cmap='gray')
plt.title("Label: {}".format(y_train[i]))
plt.xticks([])
plt.yticks([])
plt.tight_layout()
plt.show()
def extract_patches_and_vectorize(image):
patches = []
for i in range(0, 28, 7):
for j in range(0, 28, 7):
patch = image[i:i+7, j:j+7].flatten()
patches.append(patch)
return np.array(patches)
x_train_patches = np.array([extract_patches_and_vectorize(image) for image in x_train])
x_test_patches = np.array([extract_patches_and_vectorize(image) for image in x_test])
for i in range(x_train_patches.shape[1]):
plt.subplot(4, 4, i + 1)
plt.imshow(x_train_patches[0][i].reshape((7, 7)), cmap='gray')
plt.axis('off')
x_train_patches.shape
(60000, 16, 49)
x_test_patches.shape
(10000, 16, 49)
sequence_input = Input(shape=(15, 49), name='sequence_input')
lstm_layer, state_h, state_c = LSTM(64, return_sequences=True, return_state=True)(sequence_input)
dense_layer = Dense(49, activation='sigmoid')(lstm_layer)
model = Model(inputs=sequence_input, outputs=[dense_layer, state_h, state_c])
model.compile(optimizer='adam', loss=['mse', None, None])
model.summary()
Model: "model_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
sequence_input (InputLayer) [(None, 15, 49)] 0
lstm_8 (LSTM) [(None, 15, 64), 29184
(None, 64),
(None, 64)]
dense_4 (Dense) (None, 15, 49) 3185
=================================================================
Total params: 32,369
Trainable params: 32,369
Non-trainable params: 0
_________________________________________________________________
epochs = 100
batch_size=100
test_losses=[]
train_losses=[]
for epoch in range(epochs):
indices = np.arange(len(x_train_patches))
np.random.shuffle(indices)
x_train_shuffled = x_train_patches[indices]
for i in range(0, len(x_train_shuffled), batch_size):
x_batch = x_train_shuffled[i:i+batch_size,:15]
y_batch = np.roll(x_batch, shift=-1, axis=1)
loss = model.train_on_batch(x_batch, y_batch)
train_losses.append(loss)
x_test_pred,_,_ = model.predict(x_test_patches[:, :-1, :])
test_loss = np.mean(np.square(x_test_pred - x_test_patches[:, 1:, :]))
print(f"Epoch {epoch + 1}/{epochs}, Loss: {loss}, Test Loss: {test_loss}")
if epoch > 40 and test_loss > np.min(test_losses[-5:]):
print("Early stopping as test loss is increasing.")
break
test_losses.append(test_loss)
313/313 [==============================] - 1s 3ms/step Epoch 1/100, Loss: [0.06809832900762558, 0.06809832900762558], Test Loss: 0.06860233655749945 313/313 [==============================] - 1s 3ms/step Epoch 2/100, Loss: [0.05971193313598633, 0.05971193313598633], Test Loss: 0.05540381752075744 313/313 [==============================] - 1s 3ms/step Epoch 3/100, Loss: [0.049155689775943756, 0.049155689775943756], Test Loss: 0.0490882729840519 313/313 [==============================] - 1s 3ms/step Epoch 4/100, Loss: [0.043583668768405914, 0.043583668768405914], Test Loss: 0.0452417094779404 313/313 [==============================] - 1s 3ms/step Epoch 5/100, Loss: [0.04260799288749695, 0.04260799288749695], Test Loss: 0.042821531332668576 313/313 [==============================] - 1s 3ms/step Epoch 6/100, Loss: [0.03984338045120239, 0.03984338045120239], Test Loss: 0.04116290910537561 313/313 [==============================] - 1s 3ms/step Epoch 7/100, Loss: [0.041307684034109116, 0.041307684034109116], Test Loss: 0.039985981361423666 313/313 [==============================] - 1s 3ms/step Epoch 8/100, Loss: [0.041601065546274185, 0.041601065546274185], Test Loss: 0.03907890395969381 313/313 [==============================] - 1s 3ms/step Epoch 9/100, Loss: [0.03720533102750778, 0.03720533102750778], Test Loss: 0.03815129262278348 313/313 [==============================] - 1s 3ms/step Epoch 10/100, Loss: [0.035169556736946106, 0.035169556736946106], Test Loss: 0.03744707305196011 313/313 [==============================] - 1s 3ms/step Epoch 11/100, Loss: [0.036159664392471313, 0.036159664392471313], Test Loss: 0.036913974727014354 313/313 [==============================] - 1s 3ms/step Epoch 12/100, Loss: [0.03777166083455086, 0.03777166083455086], Test Loss: 0.03633953804189876 313/313 [==============================] - 1s 3ms/step Epoch 13/100, Loss: [0.037237901240587234, 0.037237901240587234], Test Loss: 0.03580938963211134 313/313 [==============================] - 1s 3ms/step Epoch 14/100, Loss: [0.036839112639427185, 0.036839112639427185], Test Loss: 0.03546162408550214 313/313 [==============================] - 1s 3ms/step Epoch 15/100, Loss: [0.03392687439918518, 0.03392687439918518], Test Loss: 0.035170756355544855 313/313 [==============================] - 1s 3ms/step Epoch 16/100, Loss: [0.03424210101366043, 0.03424210101366043], Test Loss: 0.034852461907680904 313/313 [==============================] - 1s 3ms/step Epoch 17/100, Loss: [0.036853015422821045, 0.036853015422821045], Test Loss: 0.03448111775229697 313/313 [==============================] - 1s 3ms/step Epoch 18/100, Loss: [0.033142197877168655, 0.033142197877168655], Test Loss: 0.03430786011128326 313/313 [==============================] - 1s 3ms/step Epoch 19/100, Loss: [0.03387076035141945, 0.03387076035141945], Test Loss: 0.034102649619644036 313/313 [==============================] - 1s 3ms/step Epoch 20/100, Loss: [0.03293304517865181, 0.03293304517865181], Test Loss: 0.03386371276255949 313/313 [==============================] - 1s 3ms/step Epoch 21/100, Loss: [0.0331404022872448, 0.0331404022872448], Test Loss: 0.03368659366477387 313/313 [==============================] - 1s 3ms/step Epoch 22/100, Loss: [0.03421797230839729, 0.03421797230839729], Test Loss: 0.03351205963174174 313/313 [==============================] - 1s 3ms/step Epoch 23/100, Loss: [0.03591657429933548, 0.03591657429933548], Test Loss: 0.033290133432905385 313/313 [==============================] - 1s 3ms/step Epoch 24/100, Loss: [0.03261713311076164, 0.03261713311076164], Test Loss: 0.03308561109026326 313/313 [==============================] - 1s 3ms/step Epoch 25/100, Loss: [0.031634848564863205, 0.031634848564863205], Test Loss: 0.03311631174689192 313/313 [==============================] - 1s 3ms/step Epoch 26/100, Loss: [0.0332736074924469, 0.0332736074924469], Test Loss: 0.03284754442988376 313/313 [==============================] - 1s 3ms/step Epoch 27/100, Loss: [0.03421398997306824, 0.03421398997306824], Test Loss: 0.03302887322187158 313/313 [==============================] - 1s 3ms/step Epoch 28/100, Loss: [0.03258569911122322, 0.03258569911122322], Test Loss: 0.03263145672670352 313/313 [==============================] - 1s 3ms/step Epoch 29/100, Loss: [0.03165009617805481, 0.03165009617805481], Test Loss: 0.03255170303296423 313/313 [==============================] - 1s 3ms/step Epoch 30/100, Loss: [0.03216420114040375, 0.03216420114040375], Test Loss: 0.03242600007722906 313/313 [==============================] - 1s 2ms/step Epoch 31/100, Loss: [0.0326470211148262, 0.0326470211148262], Test Loss: 0.03233932693880202 313/313 [==============================] - 1s 3ms/step Epoch 32/100, Loss: [0.03150452300906181, 0.03150452300906181], Test Loss: 0.03228155122411337 313/313 [==============================] - 1s 3ms/step Epoch 33/100, Loss: [0.03318797051906586, 0.03318797051906586], Test Loss: 0.032200338311958177 313/313 [==============================] - 1s 3ms/step Epoch 34/100, Loss: [0.032033320516347885, 0.032033320516347885], Test Loss: 0.03221852108095834 313/313 [==============================] - 1s 3ms/step Epoch 35/100, Loss: [0.030563924461603165, 0.030563924461603165], Test Loss: 0.03199768466946761 313/313 [==============================] - 1s 3ms/step Epoch 36/100, Loss: [0.03410683199763298, 0.03410683199763298], Test Loss: 0.03198335760152944 313/313 [==============================] - 1s 3ms/step Epoch 37/100, Loss: [0.03141355514526367, 0.03141355514526367], Test Loss: 0.031834263625781685 313/313 [==============================] - 1s 3ms/step Epoch 38/100, Loss: [0.032358404248952866, 0.032358404248952866], Test Loss: 0.0318891829813964 313/313 [==============================] - 1s 3ms/step Epoch 39/100, Loss: [0.03103909082710743, 0.03103909082710743], Test Loss: 0.03179608114626273 313/313 [==============================] - 1s 3ms/step Epoch 40/100, Loss: [0.030637189745903015, 0.030637189745903015], Test Loss: 0.03165253573215866 313/313 [==============================] - 1s 3ms/step Epoch 41/100, Loss: [0.03201054036617279, 0.03201054036617279], Test Loss: 0.03188435048689442 313/313 [==============================] - 1s 3ms/step Epoch 42/100, Loss: [0.03346135839819908, 0.03346135839819908], Test Loss: 0.03157552079108115 313/313 [==============================] - 1s 3ms/step Epoch 43/100, Loss: [0.031951162964105606, 0.031951162964105606], Test Loss: 0.0314946190467383 313/313 [==============================] - 1s 3ms/step Epoch 44/100, Loss: [0.032493721693754196, 0.032493721693754196], Test Loss: 0.03148161119878962 313/313 [==============================] - 1s 3ms/step Epoch 45/100, Loss: [0.031665850430727005, 0.031665850430727005], Test Loss: 0.03148469578534592 Early stopping as test loss is increasing.
model.save("q1_rnn_model.h5")
plt.plot(train_losses, label='Train Loss', color='blue')
plt.plot(test_losses, label='Test Loss', color='orange')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.title('Training and Test Losses Over Epochs')
plt.legend()
plt.show()
model= tf.keras.models.load_model("q1_rnn_model.h5")
total_digit_class =10
ten_image_10_class=[]
for one_digit in range(total_digit_class):
indices = np.where(y_test == one_digit)[0]
selected_indices = np.random.choice(indices, size=10, replace=False)
selected_images = x_test[selected_indices]
ten_image_10_class.append(selected_images)
ten_image_10_class_patches=np.array([[extract_patches_and_vectorize(ten_image_10_class[digit][image]) for image in range(10)] for digit in range(10)])
generated_images = []
for d in range(10):
for i in range(10):
image = ten_image_10_class_patches[d][i]
top_half = image[:8, :].reshape(1,8,49)
# intializing the bottom half zero
bottom_half = np.zeros_like(top_half)
input_sequence = np.concatenate([top_half, bottom_half], axis=1)
input_sequence = input_sequence.reshape((1, 16, 49))
for t in range(8, 16):
generated_bottom_half, _, _ = model.predict(input_sequence[:, :t, :])
input_sequence[:, t, :] = generated_bottom_half[:,-1,:].reshape(1,1,49)
generated_sequence = np.concatenate([top_half, input_sequence[:, 8:, :]], axis=1)
generated_images.append(generated_sequence.reshape(16,49))
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import matplotlib.pyplot as plt
def reconstruct_image(vectorized_patches):
num_patches = vectorized_patches.shape[0]
patch_size = int(np.sqrt(vectorized_patches.shape[1]))
image_size = int(np.sqrt(num_patches)) * patch_size
reconstructed_image = np.zeros((image_size, image_size))
for i in range(num_patches):
row = i // int(np.sqrt(num_patches))
col = i % int(np.sqrt(num_patches))
reconstructed_image[row * patch_size:(row + 1) * patch_size, col * patch_size:(col + 1) * patch_size] = vectorized_patches[i].reshape((patch_size, patch_size))
return reconstructed_image
rec_set= [[reconstruct_image(generated_images[j*10+i]) for i in range(10)] for j in range(10)]
def plotting_img(images,title):
fig, axs = plt.subplots(10, 10, figsize=(5, 5))
for i in range(10):
for j in range(10):
image = images[i][j]
axs[i, j].imshow(image, cmap='gray')
axs[i, j].axis('off')
plt.suptitle(title)
plt.tight_layout()
plt.show()
plotting_img(ten_image_10_class,"Original Images")
plotting_img(rec_set,"Generated Images")
import numpy as np
import matplotlib.pyplot as plt
import pickle
from tensorflow.keras.layers import Input,Conv2D, Dense, Reshape, Conv2DTranspose, Flatten, Lambda
import tensorflow as tf
from tensorflow.keras.models import Sequential, Model
with open('hw5_tr7.pkl', 'rb') as f:
train_data = pickle.load(f)
num_images_to_visualize = 10
plt.figure(figsize=(10,10))
for i in range(num_images_to_visualize):
plt.subplot(1, num_images_to_visualize, i + 1)
plt.imshow(train_data[i], cmap='gray')
plt.axis('off')
plt.show()
with open('hw5_te7.pkl', 'rb') as f:
test_data = pickle.load(f)
num_images_to_visualize = 10
plt.figure(figsize=(10,10))
for i in range(num_images_to_visualize):
plt.subplot(1, num_images_to_visualize, i + 1)
plt.imshow(test_data[i], cmap='gray')
plt.axis('off')
plt.show()
train_data.shape
(6265, 28, 28)
test_data.shape
(1028, 28, 28)
train_data = train_data.reshape((-1, 28, 28, 1))
test_data = test_data.reshape((-1, 28, 28, 1))
z_mean =0
z_log_var=0
latent_dim = 3
encoder_inputs = Input(shape=(28, 28, 1))
x = Conv2D(16, 3, activation='relu', )(encoder_inputs)
x = Conv2D(64, 3, activation='relu', )(x)
x = Flatten()(x)
x = Dense(128)(x)
z_mean = Dense(latent_dim)(x)
z_log_var = Dense(latent_dim)(x)
def sampling(args):
z_mean, z_log_var = args
batch = tf.shape(z_mean)[0]
dim = tf.shape(z_mean)[1]
epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
return z_mean + tf.exp(0.5 * z_log_var) * epsilon
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
decoder = Sequential()
decoder.add(Dense(latent_dim*2, input_shape=(latent_dim,), activation='relu'))
decoder.add(Dense(28*28, activation='relu'))
decoder.add(Reshape((28,28,1)))
decoder.add(Conv2DTranspose(64, 3, activation='relu', padding='same'))
decoder.add(Conv2DTranspose(16, 3, activation='relu', padding='same'))
decoder.add(Conv2DTranspose(1, 3, activation='sigmoid',padding="same"))
decoder_outputs=decoder(z)
vae = Model(encoder_inputs, decoder_outputs)
rec_loss = tf.keras.losses.binary_crossentropy(tf.keras.backend.flatten(encoder_inputs), tf.keras.backend.flatten(decoder_outputs))
kl_loss = -0.5 * tf.reduce_sum(1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var), axis=-1)
kl_loss = kl_loss / (784)
vae_loss = tf.keras.backend.mean(rec_loss + kl_loss)
vae.add_loss(vae_loss)
vae.compile(loss=None,optimizer='adam')
vae.fit(train_data,train_data, epochs=40, batch_size=200,shuffle=True,validation_data=(test_data,test_data))
Epoch 1/40 32/32 [==============================] - 9s 200ms/step - loss: 0.4969 - val_loss: 0.3138 Epoch 2/40 32/32 [==============================] - 6s 193ms/step - loss: 0.2798 - val_loss: 0.2512 Epoch 3/40 32/32 [==============================] - 6s 193ms/step - loss: 0.2404 - val_loss: 0.2137 Epoch 4/40 32/32 [==============================] - 6s 195ms/step - loss: 0.2095 - val_loss: 0.1960 Epoch 5/40 32/32 [==============================] - 6s 193ms/step - loss: 0.1990 - val_loss: 0.1907 Epoch 6/40 32/32 [==============================] - 6s 196ms/step - loss: 0.1946 - val_loss: 0.1865 Epoch 7/40 32/32 [==============================] - 6s 194ms/step - loss: 0.1903 - val_loss: 0.1814 Epoch 8/40 32/32 [==============================] - 6s 193ms/step - loss: 0.1858 - val_loss: 0.1774 Epoch 9/40 32/32 [==============================] - 6s 195ms/step - loss: 0.1818 - val_loss: 0.1742 Epoch 10/40 32/32 [==============================] - 6s 196ms/step - loss: 0.1795 - val_loss: 0.1736 Epoch 11/40 32/32 [==============================] - 6s 198ms/step - loss: 0.1775 - val_loss: 0.1708 Epoch 12/40 32/32 [==============================] - 6s 196ms/step - loss: 0.1757 - val_loss: 0.1698 Epoch 13/40 32/32 [==============================] - 6s 191ms/step - loss: 0.1735 - val_loss: 0.1680 Epoch 14/40 32/32 [==============================] - 6s 195ms/step - loss: 0.1718 - val_loss: 0.1663 Epoch 15/40 32/32 [==============================] - 6s 192ms/step - loss: 0.1702 - val_loss: 0.1660 Epoch 16/40 32/32 [==============================] - 6s 195ms/step - loss: 0.1686 - val_loss: 0.1638 Epoch 17/40 32/32 [==============================] - 6s 196ms/step - loss: 0.1672 - val_loss: 0.1633 Epoch 18/40 32/32 [==============================] - 6s 196ms/step - loss: 0.1662 - val_loss: 0.1621 Epoch 19/40 32/32 [==============================] - 6s 199ms/step - loss: 0.1657 - val_loss: 0.1621 Epoch 20/40 32/32 [==============================] - 6s 192ms/step - loss: 0.1648 - val_loss: 0.1615 Epoch 21/40 32/32 [==============================] - 6s 192ms/step - loss: 0.1639 - val_loss: 0.1608 Epoch 22/40 32/32 [==============================] - 6s 193ms/step - loss: 0.1631 - val_loss: 0.1605 Epoch 23/40 32/32 [==============================] - 6s 193ms/step - loss: 0.1628 - val_loss: 0.1603 Epoch 24/40 32/32 [==============================] - 6s 199ms/step - loss: 0.1621 - val_loss: 0.1598 Epoch 25/40 32/32 [==============================] - 6s 199ms/step - loss: 0.1614 - val_loss: 0.1590 Epoch 26/40 32/32 [==============================] - 6s 197ms/step - loss: 0.1613 - val_loss: 0.1591 Epoch 27/40 32/32 [==============================] - 6s 200ms/step - loss: 0.1610 - val_loss: 0.1588 Epoch 28/40 32/32 [==============================] - 6s 192ms/step - loss: 0.1603 - val_loss: 0.1589 Epoch 29/40 32/32 [==============================] - 6s 195ms/step - loss: 0.1604 - val_loss: 0.1588 Epoch 30/40 32/32 [==============================] - 6s 202ms/step - loss: 0.1600 - val_loss: 0.1588 Epoch 31/40 32/32 [==============================] - 6s 195ms/step - loss: 0.1598 - val_loss: 0.1590 Epoch 32/40 32/32 [==============================] - 6s 196ms/step - loss: 0.1596 - val_loss: 0.1587 Epoch 33/40 32/32 [==============================] - 6s 198ms/step - loss: 0.1595 - val_loss: 0.1584 Epoch 34/40 32/32 [==============================] - 6s 195ms/step - loss: 0.1591 - val_loss: 0.1585 Epoch 35/40 32/32 [==============================] - 6s 194ms/step - loss: 0.1588 - val_loss: 0.1584 Epoch 36/40 32/32 [==============================] - 6s 196ms/step - loss: 0.1585 - val_loss: 0.1578 Epoch 37/40 32/32 [==============================] - 6s 195ms/step - loss: 0.1584 - val_loss: 0.1581 Epoch 38/40 32/32 [==============================] - 6s 194ms/step - loss: 0.1579 - val_loss: 0.1581 Epoch 39/40 32/32 [==============================] - 6s 195ms/step - loss: 0.1578 - val_loss: 0.1578 Epoch 40/40 32/32 [==============================] - 6s 196ms/step - loss: 0.1579 - val_loss: 0.1576
<keras.callbacks.History at 0x7f7dfe1f7580>
vae.summary()
Model: "model_9"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_4 (InputLayer) [(None, 28, 28, 1)] 0 []
conv2d_12 (Conv2D) (None, 26, 26, 16) 160 ['input_4[0][0]']
conv2d_13 (Conv2D) (None, 24, 24, 64) 9280 ['conv2d_12[0][0]']
flatten_4 (Flatten) (None, 36864) 0 ['conv2d_13[0][0]']
dense_19 (Dense) (None, 128) 4718720 ['flatten_4[0][0]']
dense_20 (Dense) (None, 3) 387 ['dense_19[0][0]']
dense_21 (Dense) (None, 3) 387 ['dense_19[0][0]']
lambda_2 (Lambda) (None, 3) 0 ['dense_20[0][0]',
'dense_21[0][0]']
sequential_3 (Sequential) (None, 28, 28, 1) 15529 ['lambda_2[0][0]']
tf.__operators__.add_2 (TFOpLa (None, 3) 0 ['dense_21[0][0]']
mbda)
tf.math.square_1 (TFOpLambda) (None, 3) 0 ['dense_20[0][0]']
tf.math.subtract_2 (TFOpLambda (None, 3) 0 ['tf.__operators__.add_2[0][0]',
) 'tf.math.square_1[0][0]']
tf.math.exp_1 (TFOpLambda) (None, 3) 0 ['dense_21[0][0]']
tf.reshape_2 (TFOpLambda) (None,) 0 ['input_4[0][0]']
tf.reshape_3 (TFOpLambda) (None,) 0 ['sequential_3[0][0]']
tf.math.subtract_3 (TFOpLambda (None, 3) 0 ['tf.math.subtract_2[0][0]',
) 'tf.math.exp_1[0][0]']
tf.cast_1 (TFOpLambda) (None,) 0 ['tf.reshape_2[0][0]']
tf.convert_to_tensor_3 (TFOpLa (None,) 0 ['tf.reshape_3[0][0]']
mbda)
tf.math.reduce_sum_1 (TFOpLamb (None,) 0 ['tf.math.subtract_3[0][0]']
da)
tf.keras.backend.binary_crosse (None,) 0 ['tf.cast_1[0][0]',
ntropy_1 (TFOpLambda) 'tf.convert_to_tensor_3[0][0]']
tf.math.multiply_1 (TFOpLambda (None,) 0 ['tf.math.reduce_sum_1[0][0]']
)
tf.math.reduce_mean_2 (TFOpLam () 0 ['tf.keras.backend.binary_crossen
bda) tropy_1[0][0]']
tf.math.truediv_1 (TFOpLambda) (None,) 0 ['tf.math.multiply_1[0][0]']
tf.__operators__.add_3 (TFOpLa (None,) 0 ['tf.math.reduce_mean_2[0][0]',
mbda) 'tf.math.truediv_1[0][0]']
tf.math.reduce_mean_3 (TFOpLam () 0 ['tf.__operators__.add_3[0][0]']
bda)
add_loss_1 (AddLoss) () 0 ['tf.math.reduce_mean_3[0][0]']
==================================================================================================
Total params: 4,744,463
Trainable params: 4,744,463
Non-trainable params: 0
__________________________________________________________________________________________________
vae.save("q2_vae_model.h5")
num_images_to_display = 4
random_indices = np.random.choice(len(test_data), num_images_to_display, replace=False)
selected_images = test_data[random_indices]
vae_outputs = vae.predict(selected_images)
fig, axes = plt.subplots(num_images_to_display, 2, figsize=(8, 2 * num_images_to_display))
fig.suptitle('Original Images vs VAE Outputs')
for i in range(num_images_to_display):
axes[i, 0].imshow(selected_images[i].reshape(28, 28), cmap='gray')
axes[i, 0].axis('off')
axes[i, 0].set_title('Original')
axes[i, 1].imshow(vae_outputs[i].reshape(28, 28), cmap='gray')
axes[i, 1].axis('off')
axes[i, 1].set_title('VAE Output')
plt.tight_layout()
plt.show()
1/1 [==============================] - 0s 232ms/step
def plot_images(images):
n = len(images)
plt.figure(figsize=(20, 20 * n))
for i in range(n):
ax = plt.subplot(1, n, i + 1)
plt.imshow(images[i])
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_xticks([])
ax.set_yticks([])
import numpy as np
import matplotlib.pyplot as plt
def generate_and_display_images(model, latent_dim, fixed_values, varying_dim_index, num_samples=10):
varying_values = np.linspace(-5.0, 5.0, num_samples).reshape(-1, 1)
latent_codes = np.tile(fixed_values, (num_samples, 1))
latent_codes[:, varying_dim_index] = varying_values.flatten()
generated_images = model.predict(latent_codes)
plt.figure(figsize=(15, 2))
for i in range(num_samples):
ax = plt.subplot(1, num_samples, i + 1)
plt.imshow(generated_images[i, :, :, 0], cmap='gray')
ax.axis('off')
plt.show()
fixed_values = np.random.normal(size=(1, latent_dim))
generate_and_display_images(decoder, latent_dim, fixed_values, 0)
generate_and_display_images(decoder, latent_dim, fixed_values, 1)
generate_and_display_images(decoder, latent_dim, fixed_values, 2)
WARNING:tensorflow:5 out of the last 109 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f7dfdf471c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. 1/1 [==============================] - 0s 133ms/step
1/1 [==============================] - 0s 31ms/step
1/1 [==============================] - 0s 31ms/step
By Looking at the images generated by VAE in third case where 3rd latent dimention is variable we can see the the gradual effect on 7 is rotation. All other 7 looks almost similar.
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Embedding, Concatenate, LeakyReLU
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
(x_train, y_train), (_, _) = tf.keras.datasets.mnist.load_data()
x_train = (x_train.astype(np.float32) - 127.5) / 127.5 # Normalize to the range [-1, 1]
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
num_classes=10
d=100
def build_generator():
model_input =Input(shape=(110,))
x = Dense(256, activation='relu')(model_input)
x = Dense(512, activation='relu')(x)
x = Dense(1024, activation='relu')(x)
x = Dense(784, activation='tanh')(x)
generated_image = x
generator = Model(model_input, generated_image)
return generator
generator = build_generator()
def build_discriminator():
image = Input(shape=(794,))
x = Dense(1024, activation='relu')(image)
x = Dense(512, activation='relu')(x)
x = Dense(256, activation='relu')(x)
validity = Dense(1, activation='sigmoid')(x)
discriminator = Model(image, validity)
return discriminator
discriminator = build_discriminator()
discriminator.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
discriminator.trainable = False
model_input = Input(shape=(110,))
generated_image = generator(model_input)
validity = discriminator(tf.concat([generated_image, tf.slice(model_input, [0, d], [-1, 10])], axis=1))
combined_model = Model(model_input, validity)
combined_model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0001))
num_epochs = 100
batch_size = 100
half_batch = batch_size // 2
real_classification_accuracy =[]
fake_classification_accuracy =[]
for epoch in range(num_epochs):
for i in range(x_train.shape[0] // batch_size):
idx = np.random.randint(0, x_train.shape[0], half_batch)
real_images, labels = x_train[idx], y_train[idx]
real_images = real_images.reshape(-1,784)
real_labels = tf.one_hot(labels, depth=10)
real=tf.concat([real_images, real_labels], axis=1)
fake_labels_internal = tf.one_hot(np.random.randint(0, 10, half_batch), depth=num_classes)
fake_images = generator.predict(tf.concat([np.random.randn(half_batch, 100), fake_labels_internal],axis=1))
fake=tf.concat([fake_images, fake_labels_internal],axis=1)
real_labels = np.ones((half_batch, 1))
fake_labels = np.zeros((half_batch, 1))
d_loss_real, d_accuracy_real = discriminator.train_on_batch(real, real_labels)
d_loss_fake, d_accuracy_fake = discriminator.train_on_batch(fake, fake_labels)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
noise = np.random.randn(batch_size, 100)
noise_labels=tf.one_hot(np.random.randint(0, 10, batch_size), depth=num_classes)
valid_labels = np.ones((batch_size, 1))
combined_noise=tf.concat([noise, noise_labels],axis=1)
g_loss = combined_model.train_on_batch(combined_noise, valid_labels)
print(f"Epoch {epoch + 1}, Step {i + 1}/{x_train.shape[0] // batch_size}, D Loss: {d_loss}, G Loss: {g_loss}")
real_classification_accuracy.append(d_accuracy_real)
fake_classification_accuracy.append(d_accuracy_fake)
generator.save_weights('generator_weights.h5')
2/2 [==============================] - 0s 2ms/step Epoch 1, Step 1/600, D Loss: 0.6229207962751389, G Loss: 0.6282433867454529 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 2/600, D Loss: 0.4648047536611557, G Loss: 0.6132249236106873 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 3/600, D Loss: 0.4355562571436167, G Loss: 0.6157044768333435 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 4/600, D Loss: 0.4265408040955663, G Loss: 0.6208247542381287 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 5/600, D Loss: 0.40935252513736486, G Loss: 0.6279304027557373 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 6/600, D Loss: 0.4011127839330584, G Loss: 0.6474299430847168 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 7/600, D Loss: 0.39107688050717115, G Loss: 0.6743640899658203 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 8/600, D Loss: 0.37651653797365725, G Loss: 0.7076165080070496 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 9/600, D Loss: 0.3612705443520099, G Loss: 0.7441642880439758 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 10/600, D Loss: 0.3440827818121761, G Loss: 0.7894956469535828 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 11/600, D Loss: 0.3206496116472408, G Loss: 0.8439888954162598 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 12/600, D Loss: 0.2949967901222408, G Loss: 0.8985227942466736 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 13/600, D Loss: 0.27621327561791986, G Loss: 0.970511794090271 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 14/600, D Loss: 0.24839588324539363, G Loss: 1.0612506866455078 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 15/600, D Loss: 0.22480690432712436, G Loss: 1.175897240638733 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 16/600, D Loss: 0.19826844474300742, G Loss: 1.2923941612243652 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 17/600, D Loss: 0.1750017087906599, G Loss: 1.4188483953475952 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 18/600, D Loss: 0.15443000569939613, G Loss: 1.561558723449707 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 19/600, D Loss: 0.1353928055614233, G Loss: 1.7220262289047241 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 20/600, D Loss: 0.11427770741283894, G Loss: 1.8606854677200317 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 21/600, D Loss: 0.10035631060600281, G Loss: 1.9960837364196777 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 22/600, D Loss: 0.09863043203949928, G Loss: 2.0948362350463867 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 23/600, D Loss: 0.08819958753883839, G Loss: 2.1956841945648193 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 24/600, D Loss: 0.08295501302927732, G Loss: 2.26613712310791 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 25/600, D Loss: 0.07527523534372449, G Loss: 2.254051446914673 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 26/600, D Loss: 0.0710587992798537, G Loss: 2.3104448318481445 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 27/600, D Loss: 0.06823352782521397, G Loss: 2.4014711380004883 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 28/600, D Loss: 0.06433381629176438, G Loss: 2.438272714614868 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 29/600, D Loss: 0.0641005071811378, G Loss: 2.5146822929382324 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 30/600, D Loss: 0.06694856192916632, G Loss: 2.5401415824890137 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 31/600, D Loss: 0.06600713916122913, G Loss: 2.516287326812744 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 32/600, D Loss: 0.06432306580245495, G Loss: 2.6057868003845215 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 33/600, D Loss: 0.06703369878232479, G Loss: 2.6338109970092773 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 34/600, D Loss: 0.06745505146682262, G Loss: 2.6119163036346436 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 35/600, D Loss: 0.06431655026972294, G Loss: 2.5319440364837646 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 36/600, D Loss: 0.07338675949722528, G Loss: 2.506734609603882 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 37/600, D Loss: 0.06349326251074672, G Loss: 2.5358660221099854 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 38/600, D Loss: 0.06813747575506568, G Loss: 2.535708427429199 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 39/600, D Loss: 0.059131401125341654, G Loss: 2.699342727661133 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 40/600, D Loss: 0.05261272471398115, G Loss: 2.749086380004883 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 41/600, D Loss: 0.05025218799710274, G Loss: 2.8774254322052 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 42/600, D Loss: 0.046981221064925194, G Loss: 2.969346046447754 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 43/600, D Loss: 0.04087746795266867, G Loss: 2.998136043548584 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 44/600, D Loss: 0.041440549306571484, G Loss: 3.0028421878814697 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 45/600, D Loss: 0.04378501512110233, G Loss: 3.009704828262329 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 46/600, D Loss: 0.04128156672231853, G Loss: 2.961148977279663 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 47/600, D Loss: 0.04109429189702496, G Loss: 3.0803842544555664 2/2 [==============================] - 0s 2ms/step Epoch 1, Step 48/600, D Loss: 0.038645025924779475, G Loss: 3.113938093185425 2/2 [==============================] - 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0s 2ms/step Epoch 30, Step 586/600, D Loss: 0.0027367173024686053, G Loss: 6.953303337097168 1/2 [==============>...............] - ETA: 0s
IOPub message rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_msg_rate_limit`. Current values: NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec) NotebookApp.rate_limit_window=3.0 (secs)
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0s 2ms/step Epoch 40, Step 168/600, D Loss: 0.0023640941362828016, G Loss: 13.426822662353516 2/2 [==============================] - 0s 2ms/step Epoch 40, Step 169/600, D Loss: 0.0060586692416109145, G Loss: 13.33163833618164 2/2 [==============================] - 0s 2ms/step Epoch 40, Step 170/600, D Loss: 0.00019606848036346491, G Loss: 12.255300521850586 2/2 [==============================] - 0s 2ms/step Epoch 40, Step 171/600, D Loss: 0.0002997983174282126, G Loss: 12.267672538757324 2/2 [==============================] - 0s 2ms/step Epoch 40, Step 172/600, D Loss: 0.0005921057891100645, G Loss: 12.313719749450684 2/2 [==============================] - 0s 2ms/step Epoch 40, Step 173/600, D Loss: 0.0015276279300451279, G Loss: 10.1227388381958 2/2 [==============================] - 0s 2ms/step Epoch 40, Step 174/600, D Loss: 0.003037807473447174, G Loss: 9.1980562210083 2/2 [==============================] - 0s 2ms/step Epoch 40, Step 175/600, D Loss: 0.056478065047713244, G Loss: 8.587774276733398 2/2 [==============================] - 0s 2ms/step Epoch 40, Step 176/600, D Loss: 0.00809584278613329, G Loss: 8.838444709777832 2/2 [==============================] - 0s 2ms/step
IOPub message rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_msg_rate_limit`. Current values: NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec) NotebookApp.rate_limit_window=3.0 (secs)
Epoch 43, Step 283/600, D Loss: 0.023904759902507067, G Loss: 5.563587188720703 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 284/600, D Loss: 0.05536608397960663, G Loss: 5.947305202484131 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 285/600, D Loss: 0.004231163649819791, G Loss: 6.5624003410339355 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 286/600, D Loss: 0.017118172720074654, G Loss: 6.404783725738525 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 287/600, D Loss: 0.008897649822756648, G Loss: 6.373302459716797 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 288/600, D Loss: 0.0434795506298542, G Loss: 6.6209330558776855 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 289/600, D Loss: 0.004783879383467138, G Loss: 7.990825176239014 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 290/600, D Loss: 0.048953431367408484, G Loss: 7.3099565505981445 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 291/600, D Loss: 0.02191514614969492, G Loss: 6.656797885894775 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 292/600, D Loss: 0.03480369411408901, G Loss: 5.591403961181641 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 293/600, D Loss: 0.014410763513296843, G Loss: 5.8774094581604 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 294/600, D Loss: 0.00907650135923177, G Loss: 7.02857780456543 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 295/600, D Loss: 0.009944978752173483, G Loss: 7.165014266967773 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 296/600, D Loss: 0.05125004728324711, G Loss: 6.6094560623168945 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 297/600, D Loss: 0.009152543032541871, G Loss: 6.034765720367432 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 298/600, D Loss: 0.034629068337380886, G Loss: 5.549727916717529 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 299/600, D Loss: 0.01427601755131036, G Loss: 5.966257095336914 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 300/600, D Loss: 0.01949674473144114, G Loss: 7.265337944030762 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 301/600, D Loss: 0.1069117842707783, G Loss: 7.055434703826904 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 302/600, D Loss: 0.004364244407042861, G Loss: 6.034015655517578 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 303/600, D Loss: 0.024750938173383474, G Loss: 6.232686996459961 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 304/600, D Loss: 0.015022775623947382, G Loss: 7.4720234870910645 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 305/600, D Loss: 0.004454459180124104, G Loss: 8.336627006530762 2/2 [==============================] - 0s 2ms/step Epoch 43, Step 306/600, D Loss: 0.12939648632891476, G Loss: 5.436914443969727 2/2 [==============================] - 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0s 2ms/step Epoch 100, Step 581/600, D Loss: 0.5508169531822205, G Loss: 1.129482626914978 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 582/600, D Loss: 0.5157572031021118, G Loss: 1.2949938774108887 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 583/600, D Loss: 0.5064136832952499, G Loss: 1.4916512966156006 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 584/600, D Loss: 0.5080635100603104, G Loss: 1.5617623329162598 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 585/600, D Loss: 0.48119863867759705, G Loss: 1.5398577451705933 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 586/600, D Loss: 0.49076879024505615, G Loss: 1.3746695518493652 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 587/600, D Loss: 0.48808690905570984, G Loss: 1.2175326347351074 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 588/600, D Loss: 0.5884633958339691, G Loss: 1.165404200553894 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 589/600, D Loss: 0.5542925596237183, G Loss: 1.2693158388137817 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 590/600, D Loss: 0.4707682132720947, G Loss: 1.3113561868667603 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 591/600, D Loss: 0.5428499579429626, G Loss: 1.3077600002288818 2/2 [==============================] - 0s 3ms/step Epoch 100, Step 592/600, D Loss: 0.5113906860351562, G Loss: 1.2889877557754517 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 593/600, D Loss: 0.5759071707725525, G Loss: 1.283506155014038 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 594/600, D Loss: 0.44238606095314026, G Loss: 1.47328519821167 2/2 [==============================] - 0s 3ms/step Epoch 100, Step 595/600, D Loss: 0.5492995977401733, G Loss: 1.2872421741485596 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 596/600, D Loss: 0.5247913002967834, G Loss: 1.2057284116744995 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 597/600, D Loss: 0.5104585587978363, G Loss: 1.2061474323272705 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 598/600, D Loss: 0.5864528715610504, G Loss: 1.0812371969223022 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 599/600, D Loss: 0.5642553269863129, G Loss: 1.1053622961044312 2/2 [==============================] - 0s 2ms/step Epoch 100, Step 600/600, D Loss: 0.4518793225288391, G Loss: 1.1859005689620972
generator.save_weights("q3_cgan.h5")
generator.save("q3_cgan.h5")
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
plt.figure(figsize=(10, 6))
plt.plot(np.arange(num_epochs),real_classification_accuracy, color='red', label='Real Classification Accuracy')
plt.plot(np.arange(num_epochs), fake_classification_accuracy, color='blue', label='Fake Classification Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.title('Real and Fake Classification Accuracies vs Epochs')
plt.legend()
plt.show()
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def generate_test_examples(generator, num_examples_per_class=10):
fig, axes = plt.subplots(10, num_examples_per_class, figsize=(num_examples_per_class, 10))
for digit in range(10):
for i in range(num_examples_per_class):
random_vector = tf.random.normal((1, 100))
label = tf.reshape(tf.one_hot(digit, depth=num_classes),(1,num_classes))
generated_image=generator.predict(tf.concat([random_vector, label],axis=1))
axes[digit, i].imshow(generated_image.reshape(28, 28), cmap='gray')
axes[digit, i].axis('off')
plt.show()
generate_test_examples(generator)
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label = tf.reshape(tf.one_hot(6, depth=num_classes),(1,num_classes))
random_vector = tf.random.normal((1, 100))
generated_image=generator.predict(tf.concat([random_vector, label],axis=1))
plt.imshow(generated_image.reshape(28, 28), cmap='gray')
plt.axis('off')
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(-0.5, 27.5, 27.5, -0.5)
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Embedding, Concatenate, LeakyReLU, Dropout,BatchNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
(x_train,y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = (x_train.astype(np.float32) - 127.5) / 127.5
x_test = (x_test.astype(np.float32) - 127.5) / 127.5
center_patches = x_train[:, 10:20, 10:20].reshape(x_train.shape[0], 100)
def build_generator():
model_input =Input(shape=(200,))
x = Dense(200, activation='relu',kernel_initializer='HeNormal')(model_input)
x = BatchNormalization(momentum=0.5)(x)
x = Dense(400, activation='relu',kernel_initializer='HeNormal')(x)
x = BatchNormalization(momentum=0.5)(x)
x = Dense(784, activation='tanh',kernel_initializer='HeNormal')(x)
generated_image = x
generator = Model(model_input, generated_image)
return generator
generator = build_generator()
def build_discriminator():
image = Input(shape=(784,))
x = Dense(400,kernel_initializer='HeNormal')(image)
x = LeakyReLU(0.2)(x)
x = Dropout(0.1)(x)
x = Dense(200,kernel_initializer='HeNormal')(x)
x = LeakyReLU(0.2)(x)
x = Dropout(0.1)(x)
x = Dense(100,kernel_initializer='HeNormal')(x)
x = LeakyReLU(0.2)(x)
x = Dropout(0.1)(x)
validity = Dense(1, activation='sigmoid',kernel_initializer='GlorotNormal')(x)
discriminator = Model(image, validity)
return discriminator
discriminator = build_discriminator()
discriminator.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
discriminator.trainable = False
model_input = Input(shape=(200,))
generated_image = generator(model_input)
validity = discriminator(generated_image)
combined_model = Model(model_input, validity)
combined_model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0001))
lambda_ = 10
epochs = 100
batch_size = 100
real_classification_accuracy =[]
fake_classification_accuracy =[]
for epoch in range(epochs):
for i in range(x_train.shape[0] // batch_size):
idx = np.random.normal(0,1,size = (batch_size, 100))
real_image_index=i*batch_size
real_center_p=center_patches[real_image_index:real_image_index+batch_size]
gen_input = tf.concat([idx, real_center_p], axis=1)
generated_images = generator.predict(gen_input)
real_images = tf.reshape(x_train[np.random.randint(0, x_train.shape[0], batch_size)],(-1,784))
labels_real = np.ones((batch_size, 1))
labels_fake = np.zeros((batch_size, 1))
discriminator.trainable = True
d_loss_real, real_accuracy = discriminator.train_on_batch(real_images, labels_real)
d_loss_fake, fake_accuracy = discriminator.train_on_batch(generated_images, labels_fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
gen_center_patch =generated_images.reshape(batch_size, 28,28)
gen_center_patch=gen_center_patch[:, 10:20, 10:20].reshape(batch_size, 100)
idx = np.random.normal(0,1,size = (batch_size, 100))
gan_input = tf.concat([idx, center_patches[real_image_index:real_image_index+batch_size]], axis=1)
discriminator.trainable = False
g_loss = combined_model.train_on_batch(gan_input, labels_real)+(lambda_ * np.mean((gen_center_patch-real_center_p)**2))
print(f"Epoch {epoch}, D Loss: {d_loss}, G Loss: {g_loss}")
real_classification_accuracy.append(real_accuracy)
fake_classification_accuracy.append(fake_accuracy)
4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.5602330416440964, G Loss: 13.753462314605713 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.41438134014606476, G Loss: 13.44375365972519 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.3821635115891695, G Loss: 13.814990222454071 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.33656000904738903, G Loss: 13.91959285736084 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.3324525700882077, G Loss: 13.898152232170105 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.32486214861273766, G Loss: 14.283116340637207 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.2727014869451523, G Loss: 14.284304022789001 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.2855637622997165, G Loss: 14.506694793701172 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.22687891265377402, G Loss: 14.9063960313797 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.17028943821787834, G Loss: 14.845353126525879 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.19369107764214277, G Loss: 15.048650741577148 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.1421350659802556, G Loss: 15.13162386417389 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.12401079526171088, G Loss: 15.505995035171509 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.09788815677165985, G Loss: 15.463078379631042 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.09336477005854249, G Loss: 15.67944884300232 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.10030757123604417, G Loss: 15.61555552482605 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.08835186529904604, G Loss: 15.45225977897644 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.09143894352018833, G Loss: 15.777815103530884 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.060519637539982796, G Loss: 15.959132432937622 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.05482442816719413, G Loss: 16.122259378433228 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.05265358975157142, G Loss: 16.168246746063232 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.06562224263325334, G Loss: 16.263578176498413 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.0622556391172111, G Loss: 16.25364398956299 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.03464962216094136, G Loss: 16.1770601272583 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.04046581871807575, G Loss: 16.35607933998108 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.045226411428302526, G Loss: 16.159900426864624 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.03534498834051192, G Loss: 16.64219880104065 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.03348815883509815, G Loss: 16.68274688720703 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.03328073304146528, G Loss: 16.86107063293457 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.03360179578885436, G Loss: 16.429542303085327 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.033232518937438726, G Loss: 16.654917001724243 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.04182334244251251, G Loss: 16.6395845413208 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.03372060973197222, G Loss: 16.906119108200073 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.029754870338365436, G Loss: 16.968058347702026 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.023434811737388372, G Loss: 17.134265184402466 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.032753553707152605, G Loss: 17.05333948135376 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.02350772195495665, G Loss: 16.93900179862976 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.02662941487506032, G Loss: 17.077784299850464 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.019635952077805996, G Loss: 16.954172372817993 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.02094203745946288, G Loss: 17.010786294937134 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.025455265305936337, G Loss: 17.222331762313843 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.025999370962381363, G Loss: 16.84479331970215 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.019837275380268693, G Loss: 16.97710919380188 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.023199081420898438, G Loss: 17.217884302139282 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.017058408120647073, G Loss: 17.281946420669556 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.019051779061555862, G Loss: 17.67174243927002 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.020515969954431057, G Loss: 17.66363024711609 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.019802531111054122, G Loss: 17.642938375473022 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.017594633623957634, G Loss: 17.190579652786255 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.01806817203760147, G Loss: 17.6163432598114 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.015060425153933465, G Loss: 17.580235958099365 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.0186804651748389, G Loss: 17.361702919006348 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.017283879220485687, G Loss: 17.63794779777527 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.01838587294332683, G Loss: 17.610073566436768 4/4 [==============================] - 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0s 2ms/step Epoch 99, D Loss: 0.5775502920150757, G Loss: 15.5781729221344 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5299319624900818, G Loss: 15.628835439682007 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5949146449565887, G Loss: 16.00834822654724 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5766431540250778, G Loss: 15.447623133659363 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5986432731151581, G Loss: 15.410873413085938 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5339696109294891, G Loss: 15.869221568107605 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5737928152084351, G Loss: 15.830586075782776 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5586130917072296, G Loss: 15.761186718940735 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5737297534942627, G Loss: 15.737704515457153 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5716003179550171, G Loss: 15.788486003875732 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5235724747180939, G Loss: 16.075360417366028 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5801036059856415, G Loss: 16.49210226535797 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5465067327022552, G Loss: 15.220535159111023 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5843175947666168, G Loss: 15.578161835670471 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.547363817691803, G Loss: 16.22451961040497 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5387487709522247, G Loss: 15.507456183433533 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5372680723667145, G Loss: 15.80859363079071 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5701762437820435, G Loss: 15.69684612751007 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5164808481931686, G Loss: 15.803628325462341 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5869445502758026, G Loss: 16.03719437122345 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.6161738336086273, G Loss: 15.33420443534851 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.6063160300254822, G Loss: 16.02941882610321 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5612565875053406, G Loss: 15.32151734828949 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.537526547908783, G Loss: 15.556979179382324 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5893469452857971, G Loss: 15.602271497249603 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5867623388767242, G Loss: 15.087887525558472 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.614661455154419, G Loss: 15.095087766647339 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5672412514686584, G Loss: 15.690043330192566 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5917568206787109, G Loss: 15.303690075874329 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5345275104045868, G Loss: 16.76938557624817 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5367154031991959, G Loss: 15.5547354221344 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5441581010818481, G Loss: 15.090345621109009 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5177913010120392, G Loss: 16.303434133529663 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5410179197788239, G Loss: 16.477030158042908 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.582291305065155, G Loss: 16.099015474319458 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5726526379585266, G Loss: 16.41979968547821 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5671155750751495, G Loss: 16.40557825565338 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5533306300640106, G Loss: 15.271159529685974 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5457230508327484, G Loss: 15.373692512512207 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5252819955348969, G Loss: 15.71594774723053 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.547817051410675, G Loss: 15.713639378547668 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5446338653564453, G Loss: 15.72198736667633 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5709523856639862, G Loss: 15.515333771705627 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5816133320331573, G Loss: 15.934276103973389 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5525582134723663, G Loss: 15.520367860794067 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.6021345257759094, G Loss: 15.658324480056763 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5723465979099274, G Loss: 14.921679854393005 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5786631405353546, G Loss: 15.293762803077698 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5808197259902954, G Loss: 15.934469938278198 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5831331312656403, G Loss: 15.516287684440613 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.6561527848243713, G Loss: 15.083822011947632 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5717592388391495, G Loss: 15.938475489616394 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.46703895926475525, G Loss: 15.776269435882568 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5623642802238464, G Loss: 15.365004062652588 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5801880061626434, G Loss: 15.410252928733826 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5644263476133347, G Loss: 15.05997359752655 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5575350075960159, G Loss: 15.372632622718811 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5696438997983932, G Loss: 15.650875449180603 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5538642108440399, G Loss: 15.905991911888123 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.560655802488327, G Loss: 16.39380383491516 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5617403984069824, G Loss: 16.101203680038452 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5309691429138184, G Loss: 16.123337507247925 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5221669375896454, G Loss: 15.425395488739014 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5565783828496933, G Loss: 15.91914451122284 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5504821687936783, G Loss: 15.060972690582275 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5470152795314789, G Loss: 16.23099184036255 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.58278888463974, G Loss: 16.34311830997467 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5454294979572296, G Loss: 16.6012921333313 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5351754724979401, G Loss: 16.232226490974426 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5509926229715347, G Loss: 16.60356307029724 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5251783430576324, G Loss: 16.494001388549805 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5552930235862732, G Loss: 16.350127816200256 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.611598789691925, G Loss: 16.048375606536865 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.6047897785902023, G Loss: 15.667383074760437 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5073001533746719, G Loss: 16.4559189081192 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5514304339885712, G Loss: 16.231053590774536
plt.figure(figsize=(10, 6))
print(len(real_classification_accuracy))
print(len(fake_classification_accuracy))
plt.plot(np.arange(epochs),real_classification_accuracy, color='red', label='Real Classification Accuracy')
plt.plot(np.arange(epochs), fake_classification_accuracy, color='blue', label='Fake Classification Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.title('Real and Fake Classification Accuracies vs Epochs')
plt.legend()
plt.show()
100 100
generator.save_weights("q4_imputing_gan_weights_l10.h5")
generator.save("q4_imputing_gan_model_l10.h5")
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
def generate_test_examples(generator, num_examples_per_class=10):
fig, axes = plt.subplots(10, num_examples_per_class, figsize=(num_examples_per_class, 10))
for digit in range(10):
for i in range(num_examples_per_class):
idx = tf.random.normal((1, 100))
index=np.where(y_test == digit)[0][i]
generated_image=generator.predict(tf.concat([idx,x_test[index, 10:20, 10:20].reshape(1, 100)],axis=1))
axes[digit, i].imshow(generated_image.reshape(28, 28), cmap='gray')
axes[digit, i].axis('off')
plt.show()
generate_test_examples(generator)
1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 17ms/step 1/1 [==============================] - 0s 19ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 19ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 26ms/step 1/1 [==============================] - 0s 27ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step
lambda_ = 100
epochs = 100
batch_size = 100
real_classification_accuracy =[]
fake_classification_accuracy =[]
for epoch in range(epochs):
for i in range(x_train.shape[0] // batch_size):
idx = np.random.normal(0,1,size = (batch_size, 100))
real_image_index=i*batch_size
real_center_p=center_patches[real_image_index:real_image_index+batch_size]
gen_input = tf.concat([idx, real_center_p], axis=1)
generated_images = generator.predict(gen_input)
real_images = tf.reshape(x_train[np.random.randint(0, x_train.shape[0], batch_size)],(-1,784))
labels_real = np.ones((batch_size, 1))
labels_fake = np.zeros((batch_size, 1))
discriminator.trainable = True
d_loss_real, real_accuracy = discriminator.train_on_batch(real_images, labels_real)
d_loss_fake, fake_accuracy = discriminator.train_on_batch(generated_images, labels_fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
gen_center_patch =generated_images.reshape(batch_size, 28,28)
gen_center_patch=gen_center_patch[:, 10:20, 10:20].reshape(batch_size, 100)
idx = np.random.normal(0,1,size = (batch_size, 100))
gan_input = tf.concat([idx, center_patches[real_image_index:real_image_index+batch_size]], axis=1)
discriminator.trainable = False
g_loss = combined_model.train_on_batch(gan_input, labels_real)+(lambda_ * np.mean((gen_center_patch-real_center_p)**2))
print(f"Epoch {epoch}, D Loss: {d_loss}, G Loss: {g_loss}")
real_classification_accuracy.append(real_accuracy)
fake_classification_accuracy.append(fake_accuracy)
4/4 [==============================] - 0s 3ms/step Epoch 0, D Loss: 0.8920866847038269, G Loss: 133.17162829637527 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.4413633346557617, G Loss: 129.58046805858612 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.34594959020614624, G Loss: 129.64135766029358 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.272433090955019, G Loss: 130.4432049393654 4/4 [==============================] - 0s 3ms/step Epoch 0, D Loss: 0.27862132899463177, G Loss: 132.41393065452576 4/4 [==============================] - 0s 3ms/step Epoch 0, D Loss: 0.29604741372168064, G Loss: 129.85598528385162 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.23888999968767166, G Loss: 134.81821036338806 4/4 [==============================] - 0s 3ms/step Epoch 0, D Loss: 0.20457196095958352, G Loss: 131.21977698802948 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.1983452681452036, G Loss: 132.82813918590546 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.20449248747900128, G Loss: 132.8421584367752 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.20601950027048588, G Loss: 135.45024490356445 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.16048270603641868, G Loss: 134.33269822597504 4/4 [==============================] - 0s 3ms/step Epoch 0, D Loss: 0.12108681362587959, G Loss: 135.60905420780182 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.1346928128041327, G Loss: 135.80506598949432 4/4 [==============================] - 0s 3ms/step Epoch 0, D Loss: 0.11487732653040439, G Loss: 132.28244614601135 4/4 [==============================] - 0s 3ms/step Epoch 0, D Loss: 0.11574300692882389, G Loss: 134.52584195137024 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.10891595017164946, G Loss: 135.8986358642578 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.08542664628475904, G Loss: 133.81450152397156 4/4 [==============================] - 0s 3ms/step Epoch 0, D Loss: 0.09620303718838841, G Loss: 135.03642797470093 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.07220265897922218, G Loss: 136.71499967575073 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.07033374370075762, G Loss: 133.38311004638672 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.06151646375656128, G Loss: 136.4548637866974 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.07061322638764977, G Loss: 134.48628163337708 4/4 [==============================] - 0s 3ms/step Epoch 0, D Loss: 0.06254593492485583, G Loss: 133.80803656578064 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.06278248061425984, G Loss: 132.1451814174652 4/4 [==============================] - 0s 3ms/step Epoch 0, D Loss: 0.05226541915908456, G Loss: 135.78224062919617 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.047151392325758934, G Loss: 136.3758909702301 4/4 [==============================] - 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0s 2ms/step Epoch 0, D Loss: 0.02970930258743465, G Loss: 134.91975140571594 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.02742258203215897, G Loss: 135.69275379180908 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.029464131919667125, G Loss: 139.49512720108032 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.023286882205866277, G Loss: 136.14458441734314 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.023008156567811966, G Loss: 137.83080744743347 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.02965310961008072, G Loss: 134.12494564056396 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.028240815736353397, G Loss: 136.6752486228943 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.02367677609436214, G Loss: 134.7630078792572 4/4 [==============================] - 0s 2ms/step Epoch 0, D Loss: 0.026341877412050962, G Loss: 133.12475752830505 4/4 [==============================] - 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0s 2ms/step Epoch 18, D Loss: 0.46425125002861023, G Loss: 125.57795560359955 4/4 [==============================] - 0s 3ms/step Epoch 18, D Loss: 0.33190663158893585, G Loss: 125.15701222419739 4/4 [==============================] - 0s 2ms/step Epoch 18, D Loss: 0.3389836549758911, G Loss: 111.69301640987396 4/4 [==============================] - 0s 2ms/step Epoch 18, D Loss: 0.3674848601222038, G Loss: 117.8059104681015 4/4 [==============================] - 0s 2ms/step Epoch 18, D Loss: 0.3708904832601547, G Loss: 117.49287211894989 4/4 [==============================] - 0s 3ms/step Epoch 18, D Loss: 0.44122807681560516, G Loss: 116.72688519954681 4/4 [==============================] - 0s 3ms/step Epoch 18, D Loss: 0.3629736453294754, G Loss: 119.66805458068848 4/4 [==============================] - 0s 2ms/step Epoch 18, D Loss: 0.3666400611400604, G Loss: 125.75218892097473 4/4 [==============================] - 0s 2ms/step Epoch 18, D Loss: 0.37610307335853577, G Loss: 119.29668748378754 4/4 [==============================] - 0s 2ms/step Epoch 18, D Loss: 0.36846062541007996, G Loss: 117.23011696338654 4/4 [==============================] - 0s 2ms/step Epoch 18, D Loss: 0.35412250459194183, G Loss: 121.52703475952148 4/4 [==============================] - 0s 2ms/step Epoch 18, D Loss: 0.39542102813720703, G Loss: 118.10980010032654 4/4 [==============================] - 0s 2ms/step Epoch 18, D Loss: 0.4039040356874466, G Loss: 123.38511407375336 4/4 [==============================] - 0s 3ms/step Epoch 18, D Loss: 0.47300635278224945, G Loss: 120.09385740756989 4/4 [==============================] - 0s 2ms/step Epoch 18, D Loss: 0.4355833828449249, G Loss: 119.20028901100159 4/4 [==============================] - 0s 3ms/step Epoch 18, D Loss: 0.4980803281068802, G Loss: 120.07024228572845 1/4 [======>.......................] - ETA: 0s
IOPub message rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_msg_rate_limit`. Current values: NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec) NotebookApp.rate_limit_window=3.0 (secs)
Epoch 33, D Loss: 0.506032720208168, G Loss: 128.28903138637543 4/4 [==============================] - 0s 3ms/step Epoch 33, D Loss: 0.5948576331138611, G Loss: 125.5464414358139 4/4 [==============================] - 0s 2ms/step Epoch 33, D Loss: 0.49944262206554413, G Loss: 121.90185022354126 4/4 [==============================] - 0s 2ms/step Epoch 33, D Loss: 0.3831658363342285, G Loss: 118.11629045009613 4/4 [==============================] - 0s 2ms/step Epoch 33, D Loss: 0.48237253725528717, G Loss: 121.48814857006073 4/4 [==============================] - 0s 2ms/step Epoch 33, D Loss: 0.38901716470718384, G Loss: 111.88680100440979 4/4 [==============================] - 0s 2ms/step Epoch 33, D Loss: 0.49867452681064606, G Loss: 122.25502264499664 4/4 [==============================] - 0s 2ms/step Epoch 33, D Loss: 0.5057661235332489, G Loss: 129.3989623785019 4/4 [==============================] - 0s 2ms/step Epoch 33, D Loss: 0.42080117762088776, G Loss: 121.57803761959076 4/4 [==============================] - 0s 3ms/step Epoch 33, D Loss: 0.48341046273708344, G Loss: 120.31249630451202 4/4 [==============================] - 0s 2ms/step Epoch 33, D Loss: 0.518438071012497, G Loss: 127.12220764160156 4/4 [==============================] - 0s 2ms/step Epoch 33, D Loss: 0.4870556592941284, G Loss: 112.72785007953644 4/4 [==============================] - 0s 2ms/step Epoch 33, D Loss: 0.43843838572502136, G Loss: 114.63800966739655 4/4 [==============================] - 0s 2ms/step Epoch 33, D Loss: 0.45094846189022064, G Loss: 126.15760576725006 4/4 [==============================] - 0s 3ms/step Epoch 33, D Loss: 0.43906573951244354, G Loss: 126.67867410182953 4/4 [==============================] - 0s 3ms/step Epoch 33, D Loss: 0.4858494848012924, G Loss: 127.86034631729126 4/4 [==============================] - 0s 3ms/step Epoch 33, D Loss: 0.4728490114212036, G Loss: 124.74137806892395 4/4 [==============================] - 0s 2ms/step Epoch 33, D Loss: 0.4573426693677902, G Loss: 128.56435120105743 4/4 [==============================] - 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0s 2ms/step Epoch 50, D Loss: 0.5392605662345886, G Loss: 128.90186059474945 4/4 [==============================] - 0s 3ms/step Epoch 50, D Loss: 0.4834185987710953, G Loss: 127.49084496498108 4/4 [==============================] - 0s 2ms/step Epoch 50, D Loss: 0.5071106404066086, G Loss: 132.13278460502625 4/4 [==============================] - 0s 2ms/step Epoch 50, D Loss: 0.4978036433458328, G Loss: 126.78409457206726 1/4 [======>.......................] - ETA: 0s
IOPub message rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_msg_rate_limit`. Current values: NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec) NotebookApp.rate_limit_window=3.0 (secs)
Epoch 51, D Loss: 0.5389852225780487, G Loss: 126.64705991744995 4/4 [==============================] - 0s 3ms/step Epoch 51, D Loss: 0.5355975925922394, G Loss: 120.49915766716003 4/4 [==============================] - 0s 2ms/step Epoch 51, D Loss: 0.5166092067956924, G Loss: 119.32539904117584 4/4 [==============================] - 0s 3ms/step Epoch 51, D Loss: 0.5195003300905228, G Loss: 121.51606810092926 4/4 [==============================] - 0s 3ms/step Epoch 51, D Loss: 0.4949279874563217, G Loss: 121.55477631092072 4/4 [==============================] - 0s 2ms/step Epoch 51, D Loss: 0.48890823125839233, G Loss: 122.65162813663483 4/4 [==============================] - 0s 2ms/step Epoch 51, D Loss: 0.4730907827615738, G Loss: 121.12513244152069 4/4 [==============================] - 0s 3ms/step Epoch 51, D Loss: 0.5067162811756134, G Loss: 129.49329245090485 4/4 [==============================] - 0s 2ms/step Epoch 51, D Loss: 0.43939583003520966, G Loss: 117.49311792850494 4/4 [==============================] - 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0s 2ms/step Epoch 77, D Loss: 0.6637064218521118, G Loss: 128.4466701745987 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5653561353683472, G Loss: 118.89226746559143 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.5361722707748413, G Loss: 123.83854639530182 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.560793548822403, G Loss: 117.8504524230957 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5223202109336853, G Loss: 123.708979845047 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5466504991054535, G Loss: 126.52795433998108 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.5218902081251144, G Loss: 128.3589689731598 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5278889238834381, G Loss: 125.67600536346436 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.5790343880653381, G Loss: 118.56200981140137 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5370724648237228, G Loss: 125.58957612514496 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5558464825153351, G Loss: 124.84817516803741 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5633015632629395, G Loss: 118.67102885246277 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5856315493583679, G Loss: 131.18431675434113 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5722215175628662, G Loss: 126.77748262882233 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5198269486427307, G Loss: 119.33136761188507 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.505759134888649, G Loss: 126.3219758272171 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5243995636701584, G Loss: 126.06241846084595 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.576548844575882, G Loss: 118.675905585289 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5766720771789551, G Loss: 124.97039949893951 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5177538841962814, G Loss: 122.88740122318268 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.541162371635437, G Loss: 122.10852384567261 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.5650575459003448, G Loss: 122.9294924736023 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5516948699951172, G Loss: 122.2935471534729 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5902996957302094, G Loss: 116.13331484794617 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.5403072237968445, G Loss: 119.58524763584137 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.6198860108852386, G Loss: 120.76006555557251 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.6304394900798798, G Loss: 124.19093263149261 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5312921106815338, G Loss: 125.49917101860046 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.5235068500041962, G Loss: 115.20704138278961 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5403441786766052, G Loss: 130.20705437660217 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5054849535226822, G Loss: 114.73553144931793 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5523631274700165, G Loss: 120.67462253570557 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5401426255702972, G Loss: 124.33986568450928 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5413951575756073, G Loss: 125.11305439472198 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.5820913910865784, G Loss: 117.08364641666412 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.553534209728241, G Loss: 123.4034833908081 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.5713058710098267, G Loss: 134.95075607299805 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.5417024046182632, G Loss: 119.10738229751587 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.5708441436290741, G Loss: 117.86519300937653 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5277032107114792, G Loss: 117.6325306892395 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.6561062037944794, G Loss: 119.70638084411621 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5387926697731018, G Loss: 115.92438101768494 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.6069875657558441, G Loss: 104.91797590255737 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.6084194183349609, G Loss: 121.33170878887177 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.5282287001609802, G Loss: 132.32103145122528 4/4 [==============================] - 0s 3ms/step Epoch 77, D Loss: 0.4953915476799011, G Loss: 132.1839828491211 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.547530010342598, G Loss: 131.17486095428467 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5328049063682556, G Loss: 116.38717937469482 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5273738950490952, G Loss: 122.39229774475098 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5043426305055618, G Loss: 116.544557929039 4/4 [==============================] - 0s 2ms/step Epoch 77, D Loss: 0.5582660436630249, G Loss: 122.04184985160828 4/4 [==============================] - 0s 3ms/step Epoch 78, D Loss: 0.543850839138031, G Loss: 121.52046608924866 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.519103080034256, G Loss: 115.78385186195374 4/4 [==============================] - 0s 3ms/step Epoch 78, D Loss: 0.5987860858440399, G Loss: 118.99795830249786 4/4 [==============================] - 0s 3ms/step Epoch 78, D Loss: 0.49777190387248993, G Loss: 113.86040735244751 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.5428930521011353, G Loss: 115.15336084365845 4/4 [==============================] - 0s 3ms/step Epoch 78, D Loss: 0.5452589690685272, G Loss: 120.57430922985077 4/4 [==============================] - 0s 3ms/step Epoch 78, D Loss: 0.5998388528823853, G Loss: 118.37225079536438 4/4 [==============================] - 0s 3ms/step Epoch 78, D Loss: 0.5863179862499237, G Loss: 120.30094051361084 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.5351468324661255, G Loss: 121.87978613376617 4/4 [==============================] - 0s 3ms/step Epoch 78, D Loss: 0.5656881332397461, G Loss: 115.34767723083496 4/4 [==============================] - 0s 3ms/step Epoch 78, D Loss: 0.5412950217723846, G Loss: 118.1152286529541 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.5206625014543533, G Loss: 121.06081032752991 4/4 [==============================] - 0s 3ms/step Epoch 78, D Loss: 0.5273030996322632, G Loss: 133.25230383872986 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.5072599649429321, G Loss: 139.5924552679062 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.5773649215698242, G Loss: 126.68087685108185 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.5380796492099762, G Loss: 128.39459991455078 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.5518851578235626, G Loss: 120.82205581665039 4/4 [==============================] - 0s 3ms/step Epoch 78, D Loss: 0.5780963152647018, G Loss: 116.57073891162872 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.5513120591640472, G Loss: 122.54285418987274 4/4 [==============================] - 0s 3ms/step Epoch 78, D Loss: 0.5372353792190552, G Loss: 126.61942422389984 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.5425647497177124, G Loss: 129.74037104845047 4/4 [==============================] - 0s 3ms/step Epoch 78, D Loss: 0.5711527764797211, G Loss: 111.64056718349457 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.5394120812416077, G Loss: 131.7641351222992 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.5794684588909149, G Loss: 118.5837812423706 4/4 [==============================] - 0s 2ms/step Epoch 78, D Loss: 0.5564914643764496, G Loss: 129.45187652111053 4/4 [==============================] - 0s 2ms/step
IOPub message rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_msg_rate_limit`. Current values: NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec) NotebookApp.rate_limit_window=3.0 (secs)
Epoch 82, D Loss: 0.5977953374385834, G Loss: 119.70869016647339 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5605208575725555, G Loss: 115.61925172805786 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5661195814609528, G Loss: 129.7468945980072 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.556380033493042, G Loss: 118.0338921546936 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5639421939849854, G Loss: 110.8253732919693 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5146284997463226, G Loss: 117.79058992862701 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5703779458999634, G Loss: 117.09849798679352 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.563679426908493, G Loss: 123.01481354236603 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5143482089042664, G Loss: 132.5858896970749 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5182401835918427, G Loss: 118.77203512191772 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5713374018669128, G Loss: 120.98508203029633 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.6176019310951233, G Loss: 120.67655575275421 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.6214795708656311, G Loss: 120.74795424938202 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5878007262945175, G Loss: 131.10174107551575 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5383438169956207, G Loss: 114.01943683624268 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5396147072315216, G Loss: 122.38586628437042 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5362831056118011, G Loss: 127.1442893743515 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5604864954948425, G Loss: 126.80322527885437 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5497011244297028, G Loss: 120.16930937767029 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5472040921449661, G Loss: 120.43462252616882 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5483925938606262, G Loss: 126.39597237110138 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5444171726703644, G Loss: 124.99491953849792 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5276747196912766, G Loss: 122.48828983306885 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5392455607652664, G Loss: 122.50820648670197 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5480034947395325, G Loss: 125.29170906543732 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5372427701950073, G Loss: 122.69441497325897 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5648230612277985, G Loss: 121.99628353118896 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5217592418193817, G Loss: 122.28840970993042 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5562766194343567, G Loss: 127.7250292301178 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5301045626401901, G Loss: 121.46647381782532 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5697593688964844, G Loss: 125.61128461360931 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5634817481040955, G Loss: 120.47181713581085 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.49144648015499115, G Loss: 123.50839447975159 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.557254821062088, G Loss: 120.11349904537201 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5540165603160858, G Loss: 125.76343154907227 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.554540753364563, G Loss: 123.65399360656738 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.6042590141296387, G Loss: 125.79726016521454 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5451647639274597, G Loss: 127.75847887992859 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5321454703807831, G Loss: 117.94128060340881 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5341077744960785, G Loss: 125.07211184501648 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5491926223039627, G Loss: 123.45130026340485 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.4954587370157242, G Loss: 119.42842042446136 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5161882638931274, G Loss: 129.8242893218994 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.4960812032222748, G Loss: 126.53541386127472 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5955722630023956, G Loss: 119.17499077320099 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5411682426929474, G Loss: 124.60930395126343 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.6122024357318878, G Loss: 127.06758189201355 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5718152225017548, G Loss: 121.8664721250534 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5907234251499176, G Loss: 122.98541128635406 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5716334879398346, G Loss: 122.93354797363281 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5075046569108963, G Loss: 117.76299405097961 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5557273924350739, G Loss: 119.8332257270813 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.6081952154636383, G Loss: 120.7508956193924 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5452330410480499, G Loss: 114.56361794471741 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5245781540870667, G Loss: 121.2072548866272 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5626502633094788, G Loss: 119.15159153938293 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5814380347728729, G Loss: 128.13250207901 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5334976315498352, G Loss: 123.913893699646 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5306210368871689, G Loss: 113.72687208652496 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5511350333690643, G Loss: 131.88285994529724 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.6167564988136292, G Loss: 116.3102638721466 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5361506342887878, G Loss: 118.4093086719513 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5383874177932739, G Loss: 121.78031265735626 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5314312279224396, G Loss: 124.22268795967102 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.6214689612388611, G Loss: 117.19636452198029 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5741882920265198, G Loss: 125.11962676048279 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.599481999874115, G Loss: 132.16417121887207 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.608547568321228, G Loss: 118.81056678295135 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5829173624515533, G Loss: 117.55219101905823 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5419736802577972, G Loss: 124.61598372459412 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5487889051437378, G Loss: 121.52304255962372 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.566693514585495, G Loss: 116.71625709533691 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5404334664344788, G Loss: 109.79713773727417 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5344408452510834, G Loss: 122.64932119846344 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5250967890024185, G Loss: 130.0332419872284 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5273837894201279, G Loss: 132.75161755084991 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5332275927066803, G Loss: 133.61277055740356 4/4 [==============================] - 0s 2ms/step Epoch 82, D Loss: 0.5691766738891602, G Loss: 113.53371059894562 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5458772480487823, G Loss: 125.74980854988098 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5099760890007019, G Loss: 117.80002164840698 4/4 [==============================] - 0s 3ms/step Epoch 82, D Loss: 0.5221601724624634, G Loss: 124.12521028518677 4/4 [==============================] - 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0s 3ms/step Epoch 84, D Loss: 0.6030536592006683, G Loss: 115.07022655010223 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5873820781707764, G Loss: 124.7160325050354 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5106822401285172, G Loss: 121.278205037117 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5477494597434998, G Loss: 127.05226337909698 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5835939645767212, G Loss: 125.77550399303436 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5874783992767334, G Loss: 121.40333437919617 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5441976189613342, G Loss: 123.17766034603119 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5621495544910431, G Loss: 123.93776154518127 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5942949205636978, G Loss: 122.63345408439636 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5387122333049774, G Loss: 124.23202502727509 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5877509117126465, G Loss: 120.52380788326263 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.60238116979599, G Loss: 119.53903901576996 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5521001815795898, G Loss: 123.37684118747711 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5673916637897491, G Loss: 122.66540682315826 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.4583272635936737, G Loss: 126.71502447128296 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5109826326370239, G Loss: 127.54049348831177 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5828008055686951, G Loss: 125.37993907928467 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.49403485655784607, G Loss: 127.73016631603241 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.6516866683959961, G Loss: 131.9248309135437 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5977716743946075, G Loss: 122.60549294948578 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.6271060407161713, G Loss: 118.71766245365143 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5575476288795471, G Loss: 119.95125544071198 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5504422187805176, G Loss: 116.24012565612793 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.525501012802124, G Loss: 128.26351165771484 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5638479888439178, G Loss: 120.16324627399445 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.6019035279750824, G Loss: 112.70130515098572 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5571384429931641, G Loss: 120.69438827037811 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5796200931072235, G Loss: 114.56951999664307 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5501294136047363, G Loss: 122.80707442760468 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5418753325939178, G Loss: 130.9891176223755 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5721127986907959, G Loss: 119.4300172328949 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5838221311569214, G Loss: 120.61227548122406 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.6048789918422699, G Loss: 116.90192341804504 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5630792677402496, G Loss: 121.82031798362732 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5457679927349091, G Loss: 132.11455309391022 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5438947528600693, G Loss: 116.0165787935257 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.578096479177475, G Loss: 125.25846552848816 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5792098939418793, G Loss: 126.92506551742554 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5555804967880249, G Loss: 127.10310184955597 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5523870587348938, G Loss: 120.49822163581848 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5874207615852356, G Loss: 119.28214943408966 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5833606719970703, G Loss: 125.51077163219452 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5646624565124512, G Loss: 125.19975757598877 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5440968573093414, G Loss: 126.44851815700531 4/4 [==============================] - 0s 3ms/step Epoch 84, D Loss: 0.5273033529520035, G Loss: 123.6543961763382 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5949041247367859, G Loss: 126.53882431983948 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5912477672100067, G Loss: 123.1068503856659 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5728400945663452, G Loss: 120.87123048305511 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5313765704631805, G Loss: 122.1245231628418 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.563207745552063, G Loss: 127.85957098007202 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5760299265384674, G Loss: 119.70617067813873 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.534405529499054, G Loss: 125.84214055538177 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.565849244594574, G Loss: 121.462278008461 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5041151493787766, G Loss: 126.28076159954071 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5126287341117859, G Loss: 117.54788208007812 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5761918425559998, G Loss: 127.46328592300415 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5209423005580902, G Loss: 125.80548357963562 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.533722847700119, G Loss: 128.2766523361206 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.4808557480573654, G Loss: 126.26813220977783 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5850774943828583, G Loss: 115.68418681621552 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5029104799032211, G Loss: 121.59063172340393 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5172839164733887, G Loss: 121.7705180644989 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5047498047351837, G Loss: 120.62930583953857 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5051543414592743, G Loss: 129.54201424121857 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.5363452136516571, G Loss: 128.0000925064087 4/4 [==============================] - 0s 2ms/step Epoch 84, D Loss: 0.6001453399658203, G Loss: 120.72420597076416 1/4 [======>.......................] - ETA: 0s
IOPub message rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_msg_rate_limit`. Current values: NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec) NotebookApp.rate_limit_window=3.0 (secs)
Epoch 87, D Loss: 0.6060039401054382, G Loss: 133.14017832279205 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5515275597572327, G Loss: 127.43695783615112 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5530421137809753, G Loss: 127.20675110816956 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5370367765426636, G Loss: 115.30086255073547 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.6010186672210693, G Loss: 124.987952709198 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.6042293012142181, G Loss: 125.46378874778748 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5403911471366882, G Loss: 131.21337568759918 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5588454306125641, G Loss: 126.96008718013763 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5259555876255035, G Loss: 122.86189889907837 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5468654930591583, G Loss: 121.66938734054565 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5000548660755157, G Loss: 156.24447643756866 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.561118483543396, G Loss: 136.93928110599518 4/4 [==============================] - 0s 3ms/step Epoch 87, D Loss: 0.5030038505792618, G Loss: 131.31610572338104 4/4 [==============================] - 0s 3ms/step Epoch 87, D Loss: 0.5279954075813293, G Loss: 121.82593655586243 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5553209483623505, G Loss: 124.67726480960846 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5809371769428253, G Loss: 124.7240538597107 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.6128939688205719, G Loss: 124.4542887210846 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5683011114597321, G Loss: 128.6582727432251 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.4960225373506546, G Loss: 130.55432415008545 4/4 [==============================] - 0s 3ms/step Epoch 87, D Loss: 0.499904066324234, G Loss: 129.41333889961243 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5213859975337982, G Loss: 110.44053828716278 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5594665110111237, G Loss: 128.19356167316437 4/4 [==============================] - 0s 3ms/step Epoch 87, D Loss: 0.5492413938045502, G Loss: 131.2266252040863 4/4 [==============================] - 0s 3ms/step Epoch 87, D Loss: 0.5659401118755341, G Loss: 128.47769570350647 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5550122112035751, G Loss: 126.38634300231934 4/4 [==============================] - 0s 3ms/step Epoch 87, D Loss: 0.49497954547405243, G Loss: 122.796630859375 4/4 [==============================] - 0s 3ms/step Epoch 87, D Loss: 0.5336644649505615, G Loss: 127.8242678642273 4/4 [==============================] - 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0s 2ms/step Epoch 87, D Loss: 0.49280326068401337, G Loss: 124.37838566303253 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5819689333438873, G Loss: 127.31695473194122 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.6186704933643341, G Loss: 119.76648163795471 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.6132668256759644, G Loss: 132.6939071416855 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5973502099514008, G Loss: 132.01239204406738 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5877067446708679, G Loss: 128.11375987529755 4/4 [==============================] - 0s 3ms/step Epoch 87, D Loss: 0.5837298929691315, G Loss: 134.43678975105286 4/4 [==============================] - 0s 2ms/step Epoch 87, D Loss: 0.5445336699485779, G Loss: 123.96335458755493 4/4 [==============================] - 0s 3ms/step Epoch 87, D Loss: 0.5121484696865082, G Loss: 125.54023253917694 4/4 [==============================] - 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0s 2ms/step Epoch 99, D Loss: 0.5537127256393433, G Loss: 122.05151188373566 4/4 [==============================] - 0s 3ms/step Epoch 99, D Loss: 0.6320652067661285, G Loss: 129.54792273044586 4/4 [==============================] - 0s 3ms/step Epoch 99, D Loss: 0.5730875730514526, G Loss: 128.01935148239136 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5697741210460663, G Loss: 131.67802834510803 4/4 [==============================] - 0s 3ms/step Epoch 99, D Loss: 0.5845696032047272, G Loss: 129.95418643951416 4/4 [==============================] - 0s 3ms/step Epoch 99, D Loss: 0.5038215219974518, G Loss: 118.04530775547028 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5193138718605042, G Loss: 127.19904363155365 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.6242451071739197, G Loss: 126.09462976455688 4/4 [==============================] - 0s 3ms/step Epoch 99, D Loss: 0.542067289352417, G Loss: 120.44695234298706 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5361861288547516, G Loss: 133.83303678035736 4/4 [==============================] - 0s 3ms/step Epoch 99, D Loss: 0.5303831696510315, G Loss: 130.69237411022186 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5548144280910492, G Loss: 122.76519668102264 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5892033278942108, G Loss: 127.40481162071228 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5587442219257355, G Loss: 127.51542365550995 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5852125585079193, G Loss: 122.9234344959259 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5882565081119537, G Loss: 128.05684757232666 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5537773370742798, G Loss: 126.21159791946411 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.6002175807952881, G Loss: 119.65373039245605 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5218061655759811, G Loss: 124.86668121814728 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5579563081264496, G Loss: 123.87145960330963 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5328044295310974, G Loss: 119.63966107368469 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.6127289235591888, G Loss: 123.04034554958344 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5931999385356903, G Loss: 121.14658272266388 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.4857476055622101, G Loss: 129.18267238140106 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5660465359687805, G Loss: 128.30947959423065 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.49787455797195435, G Loss: 114.94396495819092 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5611265599727631, G Loss: 128.517120718956 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.638222336769104, G Loss: 118.13976764678955 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5953163206577301, G Loss: 123.96944773197174 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.6032309532165527, G Loss: 120.36501669883728 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5888164341449738, G Loss: 125.1391749382019 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5113705694675446, G Loss: 119.15396356582642 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.591776043176651, G Loss: 124.33160948753357 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5007367134094238, G Loss: 131.92298698425293 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5625384151935577, G Loss: 120.9186075925827 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.52254818379879, G Loss: 123.45078408718109 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5564556121826172, G Loss: 121.89786279201508 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5673457086086273, G Loss: 121.05667173862457 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5270674824714661, G Loss: 118.98351192474365 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5459055304527283, G Loss: 114.6704261302948 4/4 [==============================] - 0s 3ms/step Epoch 99, D Loss: 0.5965348184108734, G Loss: 127.39562606811523 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.49678297340869904, G Loss: 137.75952243804932 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5386910438537598, G Loss: 135.08362913131714 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5504991114139557, G Loss: 135.012957572937 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5251411497592926, G Loss: 116.56278789043427 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5345636904239655, G Loss: 125.8463886976242 4/4 [==============================] - 0s 2ms/step Epoch 99, D Loss: 0.5477377772331238, G Loss: 120.81942975521088 4/4 [==============================] - 0s 3ms/step Epoch 99, D Loss: 0.5231481790542603, G Loss: 122.98088681697845
plt.figure(figsize=(10, 6))
print(len(real_classification_accuracy))
print(len(fake_classification_accuracy))
plt.plot(np.arange(epochs),real_classification_accuracy, color='red', label='Real Classification Accuracy')
plt.plot(np.arange(epochs), fake_classification_accuracy, color='blue', label='Fake Classification Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.title('Real and Fake Classification Accuracies vs Epochs')
plt.legend()
plt.show()
100 100
generator.save_weights("q4_imputing_gan_weights_l100.h5")
generator.save("q4_imputing_gan_model_l100.h5")
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
def generate_test_examples(generator, num_examples_per_class=10):
fig, axes = plt.subplots(10, num_examples_per_class, figsize=(num_examples_per_class, 10))
for digit in range(10):
for i in range(num_examples_per_class):
idx = tf.random.normal((1, 100))
index=np.where(y_test == digit)[0][i]
generated_image=generator.predict(tf.concat([idx,x_test[index, 10:20, 10:20].reshape(1, 100)],axis=1))
axes[digit, i].imshow(generated_image.reshape(28, 28), cmap='gray')
axes[digit, i].axis('off')
plt.show()
generate_test_examples(generator)
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